Update volume_analyze.py

volume_analyze.py

@@ -0,0 +1,104 @@
+import pandas as pd
+import numpy as np
+from typing import Dict, Any
+from config import (
+    VOLUME_SPIKE_MULTIPLIER,
+    VOLUME_MA_PERIOD,
+    CLIMAX_VOLUME_MULTIPLIER,
+)
+
+
+def compute_volume_ma(df: pd.DataFrame, period: int = VOLUME_MA_PERIOD) -> pd.Series:
+    return df["volume"].rolling(period).mean()
+
+
+def detect_volume_spike(df: pd.DataFrame, period: int = VOLUME_MA_PERIOD) -> pd.Series:
+    vol_ma = compute_volume_ma(df, period)
+    return df["volume"] > (vol_ma * VOLUME_SPIKE_MULTIPLIER)
+
+
+def detect_climax_volume(df: pd.DataFrame, period: int = VOLUME_MA_PERIOD) -> pd.Series:
+    vol_ma = compute_volume_ma(df, period)
+    return df["volume"] > (vol_ma * CLIMAX_VOLUME_MULTIPLIER)
+
+
+def compute_breakout_confirmation(
+    df: pd.DataFrame, lookback: int = VOLUME_MA_PERIOD
+) -> pd.Series:
+    price_high = df["close"].rolling(lookback).max().shift(1)
+    price_low = df["close"].rolling(lookback).min().shift(1)
+    breakout_up = df["close"] > price_high
+    breakout_down = df["close"] < price_low
+    vol_spike = detect_volume_spike(df)
+    confirmed_up = breakout_up & vol_spike
+    confirmed_down = breakout_down & vol_spike
+    confirmation = pd.Series(0, index=df.index)
+    confirmation[confirmed_up] = 1
+    confirmation[confirmed_down] = -1
+    return confirmation
+
+
+def compute_obv(df: pd.DataFrame) -> pd.Series:
+    direction = np.sign(df["close"].diff())
+    direction.iloc[0] = 0
+    obv = (df["volume"] * direction).cumsum()
+    return obv
+
+
+def compute_volume_delta_approx(df: pd.DataFrame) -> pd.Series:
+    body = df["close"] - df["open"]
+    wick_range = (df["high"] - df["low"]).replace(0, np.nan)
+    buy_ratio = (body / wick_range).clip(0, 1).fillna(0.5)
+    buy_volume = df["volume"] * buy_ratio
+    sell_volume = df["volume"] * (1 - buy_ratio)
+    delta = buy_volume - sell_volume
+    return delta
+
+
+def analyze_volume(df: pd.DataFrame) -> Dict[str, Any]:
+    vol_ma = compute_volume_ma(df, VOLUME_MA_PERIOD)
+    spike = detect_volume_spike(df, VOLUME_MA_PERIOD)
+    climax = detect_climax_volume(df, VOLUME_MA_PERIOD)
+    breakout = compute_breakout_confirmation(df, VOLUME_MA_PERIOD)
+    obv = compute_obv(df)
+    delta = compute_volume_delta_approx(df)
+
+    last_vol = df["volume"].iloc[-1]
+    last_vol_ma = vol_ma.iloc[-1]
+    last_spike = spike.iloc[-1]
+    last_climax = climax.iloc[-1]
+    last_breakout = breakout.iloc[-1]
+
+    vol_ratio = last_vol / last_vol_ma if last_vol_ma > 0 else 1.0
+
+    obv_slope = (obv.iloc[-1] - obv.iloc[-5]) / (obv.iloc[-5] + 1e-10)
+    delta_sum = delta.iloc[-5:].sum()
+
+    if last_climax:
+        volume_score = 0.4
+    elif last_spike and last_breakout != 0:
+        volume_score = 1.0
+    elif last_spike:
+        volume_score = 0.7
+    elif vol_ratio > 1.2:
+        volume_score = 0.5
+    elif vol_ratio > 0.8:
+        volume_score = 0.3
+    else:
+        volume_score = 0.1
+
+    obv_bonus = 0.1 if obv_slope > 0 else -0.1
+    volume_score = float(np.clip(volume_score + obv_bonus, 0.0, 1.0))
+
+    return {
+        "vol_ratio": vol_ratio,
+        "spike": bool(last_spike),
+        "climax": bool(last_climax),
+        "breakout": int(last_breakout),
+        "obv_slope": obv_slope,
+        "delta_sum": delta_sum,
+        "volume_score": volume_score,
+        "spike_series": spike,
+        "climax_series": climax,
+        "breakout_series": breakout,
+    }